Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
User interfaces and discrete event simulation models
Simulation Practice and Theory
AgentSpeak(L): BDI agents speak out in a logical computable language
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Object-Oriented and Agent-Oriented Simulation: Implications for Social Science Application
Social Science Microsimulation [Dagstuhl Seminar, May, 1995]
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Decision-making in an embedded reasoning system
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Distributed patient scheduling in hospitals
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Measuring Complexity of Multi-agent Simulations --- An Attempt Using Metrics
Languages, Methodologies and Development Tools for Multi-Agent Systems
ELEMENTS OF A DOCUMENTATION FRAMEWORK FOR AGENT-BASED SIMULATION MODELS
Cybernetics and Systems - BEST OF AGENT-BASED MODELING AND SIMULATION 2008
Environmental support for tag interactions
E4MAS'06 Proceedings of the 3rd international conference on Environments for multi-agent systems III
Regulation function of the environment in agent-based simulation
E4MAS'06 Proceedings of the 3rd international conference on Environments for multi-agent systems III
Testing AGVs in dynamic warehouse environments
E4MAS'05 Proceedings of the 2nd international conference on Environments for Multi-Agent Systems
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Multi-agent Simulation can be seen as simulated multi-agent systems situated in a simulated environment. Thus, in simulations the modelled environment should always be a first order object that is as carefully developed as the agents themselves. This is especially true for evolutionary simulation and simulation of adaptive multi-agent systems, as the agents environment guides the selection and adaptation process. Also, for the simulation of realistic agent behavior complex and valid environmental models have to be tackled. Therefore, a modelling and simulation system should provide appropriate means for representing the environmental status, including spatial representations, and dynamics. On the other side, simulation infrastructure should be as simple as possible, as a modeler with domain expertise is usually no computer scientist. He might neither be trained in dealing with data structures and efficient algorithms, nor in traditional programming. After going into the details of simulated environments for multi-agent simulations, this paper shows how environments with different characteristics can be represented in a particular modelling and simulation system, named SeSAm, without asking too much from its users.